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FUHA model learns compact motion embeddings for real-time human animation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed FUHA, a novel feed-forward transformer model designed for real-time human animation. FUHA learns a compact motion embedding that captures articulation for the face, hands, and body, while disentangling motion from appearance. This approach eliminates the need for external pose estimators or parametric body models during inference. The model achieves impressive performance, running at up to 38.9 FPS at 512x512 resolution on a single RTX 4090 GPU and outperforming baselines on self-reenactment metrics. AI

IMPACT Enables more efficient and realistic human animation for applications in gaming, film, and virtual reality.

RANK_REASON This is a research paper detailing a new model and its performance. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

FUHA model learns compact motion embeddings for real-time human animation

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This is a research paper detailing a new model and its performance. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Seonwook Park, Hyunsoo Cha, Amrita Mazumdar, Shengze Wang, Tianye Li, Hanbyul Joo, Koki Nagano, Shalini De Mello ·

    Compact Motion Embeddings for Feed-forward Unified Human Animation

    arXiv:2611.07481v1 Announce Type: new Abstract: Animating a reference image with a driver's motion requires a motion representation that captures face, hand, and body articulation but excludes the driver's appearance. Most body animation methods take motion from external pose est…

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